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Thomas A. Jones, Jason A. Otkin, David J. Stensrud, and Kent Knopfmeier

Abstract

An observing system simulation experiment is used to examine the impact of assimilating water vapor–sensitive satellite infrared brightness temperatures and Doppler radar reflectivity and radial velocity observations on the analysis accuracy of a cool season extratropical cyclone. Assimilation experiments are performed for four different combinations of satellite, radar, and conventional observations using an ensemble Kalman filter assimilation system. Comparison with the high-resolution “truth” simulation indicates that the joint assimilation of satellite and radar observations reduces errors in cloud properties compared to the case in which only conventional observations are assimilated. The satellite observations provide the most impact in the mid- to upper troposphere, whereas the radar data also improve the cloud analysis near the surface and aloft as a result of their greater vertical resolution and larger overall sample size. Errors in the wind field are also significantly reduced when radar radial velocity observations were assimilated. Overall, assimilating both satellite and radar data creates the most accurate model analysis, which indicates that both observation types provide independent and complimentary information and illustrates the potential for these datasets for improving mesoscale model analyses and ensuing forecasts.

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A. C. Poje, M. Toner, A. D. Kirwan Jr., and C. K. R. T. Jones

Abstract

A basin-scale, reduced-gravity model is used to study how drifter launch strategies affect the accuracy of Eulerian velocity fields reconstructed from limited Lagrangian data. Optimal dispersion launch sites are found by tracking strongly hyperbolic singular points in the flow field. Lagrangian data from drifters launched from such locations are found to provide significant improvement in the reconstruction accuracy over similar but randomly located initial deployments. The eigenvalues of the hyperbolic singular points in the flow field determine the intensity of the local particle dispersion and thereby provide a natural timescale for initializing subsequent launches. Aligning the initial drifter launch in each site along an outflowing manifold ensures both high initial particle dispersion and the eventual sampling of regions of high kinetic energy, two factors that substantially affect the accuracy of the Eulerian reconstruction. Reconstruction error is reduced by a factor of ∼2.5 by using a continual launch strategy based on both the local stretching rates and the outflowing directions of two strong saddles located in the dynamically active region south of the central jet. Notably, a majority of those randomly chosen launch sites that produced the most accurate reconstructions also sampled the local manifold structure.

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Rebecca M. Cintineo, Jason A. Otkin, Thomas A. Jones, Steven Koch, and David J. Stensrud

Abstract

This study uses an observing system simulation experiment to explore the impact of assimilating GOES-R Advanced Baseline Imager (ABI) 6.95-μm brightness temperatures and Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity observations in an ensemble data assimilation system. A high-resolution truth simulation was used to create synthetic radar and satellite observations of a severe weather event that occurred across the U.S. central plains on 4–5 June 2005. The experiment employs the Weather Research and Forecasting Model at 4-km horizontal grid spacing and the ensemble adjustment Kalman filter algorithm in the Data Assimilation Research Testbed system. The ability of GOES-R ABI brightness temperatures to improve the analysis and forecast accuracy when assimilated separately or simultaneously with Doppler radar reflectivity and radial velocity observations was assessed, along with the use of bias correction and different covariance localization radii for the brightness temperatures. Results show that the radar observations accurately capture the structure of a portion of the storm complex by the end of the assimilation period, but that more of the storms and atmospheric features are reproduced and the accuracy of the ensuing forecast improved when the brightness temperatures are also assimilated.

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Thomas A. Jones, Jason A. Otkin, David J. Stensrud, and Kent Knopfmeier

Abstract

In the first part of this study, Jones et al. compared the relative skill of assimilating simulated radar reflectivity and radial velocity observations and satellite 6.95-μm brightness temperatures T B and found that both improved analyses of water vapor and cloud hydrometeor variables for a cool-season, high-impact weather event across the central United States. In this study, the authors examine the impact of the observations on 1–3-h forecasts and provide additional analysis of the relationship between simulated satellite and radar data observations to various water vapor and cloud hydrometeor variables. Correlation statistics showed that the radar and satellite observations are sensitive to different variables. Assimilating 6.95-μm T B primarily improved the atmospheric water vapor and frozen cloud hydrometeor variables such as ice and snow. Radar reflectivity proved more effective in both the lower and midtroposphere with the best results observed for rainwater, graupel, and snow. The impacts of assimilating both datasets decrease rapidly as a function of forecast time. By 1 h, the effects of satellite data become small on forecast cloud hydrometeor values, though it remains useful for atmospheric water vapor. The impacts of radar data last somewhat longer, sometimes up to 3 h, but also display a large decrease in effectiveness by 1 h. Generally, assimilating both satellite and radar data simultaneously generates the best analysis and forecast for most cloud hydrometeor variables.

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G. M. Martin, M. A. Ringer, V. D. Pope, A. Jones, C. Dearden, and T. J. Hinton

Abstract

The atmospheric component of the new Hadley Centre Global Environmental Model (HadGEM1) is described and an assessment of its mean climatology presented. HadGEM1 includes substantially improved representations of physical processes, increased functionality, and higher resolution than its predecessor, the Third Hadley Centre Coupled Ocean–Atmosphere General Circulation Model (HadCM3). Major developments are the use of semi-Lagrangian instead of Eulerian advection for both dynamical and tracer fields; new boundary layer, gravity wave drag, microphysics, and sea ice schemes; and major changes to the convection, land surface (including tiled surface characteristics), and cloud schemes. There is better coupling between the atmosphere, land, ocean, and sea ice subcomponents and the model includes an interactive aerosol scheme, representing both the first and second indirect effects. Particular focus has been placed on improving the processes (such as clouds and aerosol) that are most uncertain in projections of climate change.

These developments lead to a significantly more realistic simulation of the processes represented, the most notable improvements being in the hydrological cycle, cloud radiative properties, the boundary layer, the tropopause structure, and the representation of tracers.

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T. Auligné, A. Lorenc, Y. Michel, T. Montmerle, A. Jones, M. Hu, and J. Dudhia

Abstract

No Abstract available.

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D. Hudson, A. G. Marshall, O. Alves, G. Young, D. Jones, and A. Watkins

Abstract

There has been increasing demand in Australia for extended-range forecasts of extreme heat events. An assessment is made of the subseasonal experimental guidance provided by the Bureau of Meteorology’s seasonal prediction system, Predictive Ocean Atmosphere Model for Australia (POAMA, version 2), for the three most extreme heat events over Australia in 2013, which occurred in January, March, and September. The impacts of these events included devastating bushfires and damage to crops. The outlooks performed well for January and September, with forecasts indicating increased odds of top-decile maximum temperature over most affected areas at least one week in advance for the fortnightly averaged periods at the start of the heat waves and for forecasts of the months of January and September. The March event was more localized, affecting southern Australia. Although the anomalously high sea surface temperature around southern Australia in March (a potential source of predictability) was correctly forecast, the forecast of high temperatures over the mainland was restricted to the coastline. September was associated with strong forcing from some large-scale atmospheric climate drivers known to increase the chance of having more extreme temperatures over parts of Australia. POAMA-2 was able to forecast the sense of these drivers at least one week in advance, but their magnitude was weaker than observed. The reasonably good temperature forecasts for September are likely due to the model being able to forecast the important climate drivers and their teleconnection to Australian climate. This study adds to the growing evidence that there is significant potential to extend and augment traditional weather forecast guidance for extreme events to include longer-lead probabilistic information.

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R. A. Morrow, Ian S. F. Jones, R. L. Smith, and P. J. Stabeno

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No abstract available.

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Robert Meneghini, Hyokyung Kim, Liang Liao, Jeffrey A. Jones, and John M. Kwiatkowski

Abstract

It has long been recognized that path-integrated attenuation (PIA) can be used to improve precipitation estimates from high-frequency weather radar data. One approach that provides an estimate of this quantity from airborne or spaceborne radar data is the surface reference technique (SRT), which uses measurements of the surface cross section in the presence and absence of precipitation. Measurements from the dual-frequency precipitation radar (DPR) on the Global Precipitation Measurement (GPM) satellite afford the first opportunity to test the method for spaceborne radar data at Ka band as well as for the Ku-band–Ka-band combination.

The study begins by reviewing the basis of the single- and dual-frequency SRT. As the performance of the method is closely tied to the behavior of the normalized radar cross section (NRCS or σ0) of the surface, the statistics of σ0 derived from DPR measurements are given as a function of incidence angle and frequency for ocean and land backgrounds over a 1-month period. Several independent estimates of the PIA, formed by means of different surface reference datasets, can be used to test the consistency of the method since, in the absence of error, the estimates should be identical. Along with theoretical considerations, the comparisons provide an initial assessment of the performance of the single- and dual-frequency SRT for the DPR. The study finds that the dual-frequency SRT can provide improvement in the accuracy of path attenuation estimates relative to the single-frequency method, particularly at Ku band.

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Sijie Pan, Jidong Gao, Thomas A. Jones, Yunheng Wang, Xuguang Wang, and Jun Li

Abstract

With the launch of GOES-16 in November 2016, effective utilization of its data in convective-scale numerical weather prediction (NWP) has the potential to improve high-impact weather (HIWeather) forecasts. In this study, the impact of satellite-derived Layered Precipitable Water (LPW) and Cloud Water Path (CWP) in addition to NEXRAD radar observations on short-term convective scale NWP forecasts are examined using three severe weather cases that occurred in May 2017. In each case, satellite-derived CWP and LPW products and radar observations are assimilated into the Advanced Research Weather Research and Forecasting (WRF-ARW) model using the NSSL hybrid Warn-on-Forecast (WoF) analysis and forecast system. The system includes two components, the GSI-EnKF system, and a deterministic 3DEnVAR system. This study examines deterministic 0-6 h forecasts launched from the hybrid 3DEnVAR analyses for the three severe weather events. Three types of experiments are conducted and compared: (i) the control experiment (CTRL) without assimilating any data, (ii) the radar experiment (RAD) with the assimilation of radar and surface observations, and (iii) the satellite experiment (RADSAT) with the assimilation of all observations including surface, radar and satellite derived CWP and LPW. The results show that assimilating additional GOES products improves short-range forecasts by providing more accurate initial conditions, especially for moisture and temperature variables.

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